1. Since this is a single-name stock, what is the exercise style and how are you converting to Euro-style if it is American-style?

2. Are you using exclusively out-of-the-money options to calculate IV's? (I would, so puts for K < S0, calls for K > S0).

3. Assuming Euro-style or a reasonable conversion to that exercise style, are you then enforcing put-call parity and, if so, how? The CBOE has a method in the "VIX white paper", which will yield an option implied forward price. I would calculate that and compare to the futures price. (BTW, from where are you getting that futures price?) If they are different, try re-running the whole analysis with that option-implied forward price (new IV's, new SVI run, etc).

1. These are european options2. Yes, I'm using OTMs exclusively3. Although I havent enforced put call parity conditions, I checked and PCP holds for the prices above if i include transaction costs and spreadsAs far as the data is concerned, these data points are in sync and have been taken from a TBT feed

Well, it sounds like you have proceeded carefully. Since the SVI fit is certainly not guaranteed to lie between the market bid-ask, it seems like you were right in your observation that it's just poor performance. The Fengler method fit is likely much better, and fomisha just posted another one to check out.

1. Since this is a single-name stock, what is the exercise style and how are you converting to Euro-style if it is American-style?

2. Are you using exclusively out-of-the-money options to calculate IV's? (I would, so puts for K < S0, calls for K > S0).

3. Assuming Euro-style or a reasonable conversion to that exercise style, are you then enforcing put-call parity and, if so, how? The CBOE has a method in the "VIX white paper", which will yield an option implied forward price. I would calculate that and compare to the futures price. (BTW, from where are you getting that futures price?) If they are different, try re-running the whole analysis with that option-implied forward price (new IV's, new SVI run, etc).

1. These are european options2. Yes, I'm using OTMs exclusively3. Although I havent enforced put call parity conditions, I checked and PCP holds for the prices above if i include transaction costs and spreadsAs far as the data is concerned, these data points are in sync and have been taken from a TBT feed

Well, it sounds like you have proceeded carefully. Since the SVI fit is certainly not guaranteed to lie between the market bid-ask, it seems like you were right in your observation that it's just poor performance. The Fengler method fit is likely much better, and fomisha just posted another one to check out.

Another question.I came across this, where the author discusses natural SVI, SSVI and SVI-JW in addition to raw SVI. Now since the parameters of Natual SVI and SVI-JW are calculated from the parameters of raw SVI, i assume that when we optimize of raw svi, we are optimizing on the other two as well. If so, then what is the additional advantage of natural SVI and SVI-JW?PS: I consider only a fixed expiry vol curve not the vol surface, so t is fixed.

AFAIK the other two parameterizations, equivalent to the original, is more intuitive as regards to the meaning of the parameters.

The surface SVI is guaranteed to be arbitrage free (which the original is not in certain cases).

But if you need to match the market perfectly, regardless of the use or uselessness of matching perfectly, then SVI is not for you probably (except maybe for extrapolation) and using cubic spline is the way to go.

I tried to fit the raw SVI with the Quasi Explicit Method by Zeliade. The parameters turned out to be too unstable for reference use in real trading (though the model does fit well).

The problem seemed to be that there are too many local min's when using iterations in the second step to get the optimal m & sigma. As a result, by minimizing the squared error, I could get two totally different sets of parameters with two data sets only ten seconds from each other.

P.S. have you tried to calibrate the SSVI? A simple (2 parameters) while generic (one uses his own underlying model) way to model the whole surface, sounds like a winner to me.

I tried to fit the raw SVI with the Quasi Explicit Method by Zeliade. The parameters turned out to be too unstable for reference use in real trading (though the model does fit well).

The problem seemed to be that there are too many local min's when using iterations in the second step to get the optimal m & sigma. As a result, by minimizing the squared error, I could get two totally different sets of parameters with two data sets only ten seconds from each other.

P.S. have you tried to calibrate the SSVI? A simple (2 parameters) while generic (one uses his own underlying model) way to model the whole surface, sounds like a winner to me.

I tried to fit the raw SVI with the Quasi Explicit Method by Zeliade. The parameters turned out to be too unstable for reference use in real trading (though the model does fit well).

The problem seemed to be that there are too many local min's when using iterations in the second step to get the optimal m & sigma. As a result, by minimizing the squared error, I could get two totally different sets of parameters with two data sets only ten seconds from each other.

What optimizer do you use? I made a quick test on the Euro Stoxx 50 option settle prices as of 2017-08-08, 2017-07-07 and 2017-08-04, and the m, sigma obtained using the Quasi Explicit Method by Zeliade are quite stables.